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Field
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“Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of
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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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: Applied mathematics; Machine Learning; Mathematical Modelling Appl Deadline: 2026/03/24 10:59 PM UnitedKingdomTime (posted 2026/03/18 04:00 AM UnitedKingdomTime, listed until 2026/04/01 04:59 AM
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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models Foundation models represent a breakthrough in AI, as did the shift from traditional machine learning to deep learning. Numerous models become available in the field of Earth Observation and can be
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studies of physics‑informed control of mobile manipulators, data collection from real and simulated machines, and model development and testing in simulated environments. The project offers close
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research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical